{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "1.使用matplotlib完成折线图，散点图，条形图，直方图，饼状图绘制",
   "id": "f9b60b289ca3f879"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-01-06T10:56:50.492179Z",
     "start_time": "2025-01-06T10:56:49.009861Z"
    }
   },
   "source": [
    "#导入模块\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#绘制折线图\n",
    "x = range(1, 8)  # x轴的位置\n",
    "y = [17, 17, 18, 15, 11, 11, 13]\n",
    "# 传入x和y, 通过plot画折线图\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:00:30.478261Z",
     "start_time": "2025-01-06T11:00:30.357380Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#绘制散点图\n",
    "#3月份每天最高气温\n",
    "from matplotlib import font_manager\n",
    "\n",
    "font = 'C:\\\\Windows\\\\Fonts\\\\simsun.ttc'\n",
    "y = [11, 17, 16, 11, 12, 11, 12, 6, 6, 7, 8, 9, 12, 15, 14, 17, 18, 21, 16, 17, 20, 14,\n",
    "     15, 15, 15, 19, 21, 22, 22, 22,\n",
    "     23]\n",
    "x = range(1, 32)\n",
    "# 设置图形大小\n",
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "# 使用 scatter 绘制散点图\n",
    "plt.scatter(x, y, label='3 月份')\n",
    "# 调整 x 轴的刻度\n",
    "my_font = font_manager.FontProperties(fname=font, size=10)\n",
    "_xticks_labels = ['3月{}日'.format(i) for i in x]\n",
    "plt.xticks(x[::3], _xticks_labels[::3], fontproperties=my_font, rotation=45)\n",
    "plt.xlabel('日期', fontproperties=my_font)\n",
    "plt.ylabel('温度', fontproperties=my_font)\n",
    "# 图 例\n",
    "plt.legend(prop=my_font)\n",
    "plt.show()"
   ],
   "id": "cbcf55d90f28bf10",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:01:10.566857Z",
     "start_time": "2025-01-06T11:01:10.453056Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#绘制条形图\n",
    "\"\"\"\n",
    "假设你获取到了 2019 年内地电影票房前 20 的电影（列表 a)和电影票房数据（列表 b)， 请展示该数\n",
    "据 a= ['流浪地球','疯狂的外星人','飞驰人生','大黄蜂','熊出没·原始时代','新喜剧之王']\n",
    "b = [38.13,19.85,14.89,11.36,6.47,5.93]\n",
    "\"\"\"\n",
    "font = 'C:\\\\Windows\\\\Fonts\\\\simsun.ttc'\n",
    "my_font = font_manager.FontProperties(fname=font, size=16)\n",
    "a = ['流浪地球', '疯狂的外星人', '飞驰人生', '大黄蜂', '熊出没·原始时代', '新喜剧之王']\n",
    "b = [38.13, 19.85, 14.89, 11.36, 6.47, 5.93]\n",
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "# 绘制条形图的方法\n",
    "rects = plt.bar(range(len(a)), b, width=0.3, color='r')\n",
    "plt.xticks(range(len(a)), a, fontproperties=my_font, rotation=45)\n",
    "# 在条形图上加标注(水平居中)\n",
    "for rect in rects:\n",
    "    height = rect.get_height()\n",
    "    plt.text(rect.get_x() + rect.get_width() / 2, height + 0.5, str(height), ha=\"center\")\n",
    "plt.show()"
   ],
   "id": "8591a0a0642b4f4a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:02:46.720574Z",
     "start_time": "2025-01-06T11:02:46.495458Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#直方图\n",
    "'''\n",
    "现有 250 部电影的时长， 希望统计出这些电影时长的分布状态(比如时长为 100 分钟到 120 分钟电影\n",
    "的数量， 出现的频率)等信息，你应该如何呈现这些数据？\n",
    "'''\n",
    "# 1） 准备数据\n",
    "time = [131, 98, 125, 131, 124, 139, 131, 117, 128, 108, 135, 138, 131, 102, 107, 114, 119, 128, 121, 142, 127, 130,\n",
    "        124, 101, 110, 116, 117, 110, 128, 128, 115, 99, 136, 126, 134, 95, 138, 117, 111, 78, 132, 124, 113, 150, 110,\n",
    "        117, 86, 95, 144, 105, 126, 130, 126, 130, 126, 116, 123, 106, 112, 138, 123, 86, 101, 99, 136, 123, 117, 119,\n",
    "        105, 137, 123, 128, 125, 104, 109, 134, 125, 127, 105, 120, 107, 129, 116, 108, 132, 103, 136, 118, 102, 120,\n",
    "        114, 105, 115, 132, 145, 119, 121, 112, 139, 125, 138, 109, 132, 134, 156, 106, 117, 127, 144, 139, 139, 119,\n",
    "        140, 83, 110, 102, 123, 107, 143, 115, 136, 118, 139, 123, 112, 118, 125, 109, 119, 133, 112, 114, 122, 109,\n",
    "        106, 123, 116, 131, 127, 115, 118, 112, 135, 115, 146, 137, 116, 103, 144, 83, 123, 111, 110, 111, 100, 154,\n",
    "        136, 100, 118, 119, 133, 134, 106, 129, 126, 110, 111, 109, 141, 120, 117, 106, 149, 122, 122, 110, 118, 127,\n",
    "        121, 114, 125, 126, 114, 140, 103, 130, 141, 117, 106, 114, 121, 114, 133, 137, 92, 121, 112, 146, 97, 137, 105,\n",
    "        98, 117, 112, 81, 97, 139, 113, 134, 106, 144, 110, 137, 137, 111, 104, 117, 100, 111, 101, 110, 105, 129, 137,\n",
    "        112, 120, 113, 133, 112, 83, 94, 146, 133, 101, 131, 116, 111, 84, 137, 115, 122, 106, 144, 109, 123, 116, 111,\n",
    "        111, 133, 150]\n",
    "my_font = font_manager.FontProperties(fname='C:\\\\Windows\\\\Fonts\\\\simsun.ttc', size=18)\n",
    "# 创建画布\n",
    "plt.figure(figsize=(20, 8), dpi=100)  # 3） 绘制直方图\n",
    "# 设置组距\n",
    "distance = 2\n",
    "# 计算组数\n",
    "group_num = int((max(time) - min(time)) / distance)\n",
    "# 绘制直方图\n",
    "plt.hist(time, bins=group_num)\n",
    "# 修改 x 轴刻度显示\n",
    "plt.xticks(range(min(time), max(time))[::2])\n",
    "# 添加网格显示\n",
    "plt.grid(linestyle=\"--\", alpha=0.5)\n",
    "# 添加x,y轴描述信息\n",
    "plt.xlabel(\"电影时长大小\", fontproperties=my_font)\n",
    "plt.ylabel(\"电影的数据量\", fontproperties=my_font)\n",
    "# 显示图像\n",
    "plt.show()"
   ],
   "id": "dc274d86037597c6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:03:36.545521Z",
     "start_time": "2025-01-06T11:03:36.445280Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#饼图\n",
    "import matplotlib\n",
    "\n",
    "label_list = [\"第一部分\", \"第二部分\", \"第三部分\"]  # 各部分标签\n",
    "size = [55, 35, 10]  # 各部分大小\n",
    "color = [\"red\", \"green\", \"blue\"]  # 各部分颜色\n",
    "explode = [0, 0.05, 0]  # 各部分突出值\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "patches, l_text, p_text = plt.pie(size, explode=explode, colors=color, labels=label_list,\n",
    "                                  labeldistance=1.1, autopct=\"%1.1f%%\", shadow=False, startangle=90, pctdistance=0.6)\n",
    "plt.axis(\"equal\")  # 设置横轴和纵轴大小相等， 这样饼才是圆的\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "d2be311ee4a84c6a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "2.完成Numpy的数组和原生python的性能对比",
   "id": "de3eef7baad045cb"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:04:37.530805Z",
     "start_time": "2025-01-06T11:04:17.386224Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import random\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "a = []\n",
    "for i in range(100000000):\n",
    "    a.append(random.random())\n",
    "t1 = time.time()\n",
    "sum1 = sum(a)\n",
    "t2 = time.time()\n",
    "b = np.array(a)\n",
    "t4 = time.time()\n",
    "sum3 = np.sum(b)\n",
    "t5 = time.time()\n",
    "print(t2 - t1, t5 - t4)"
   ],
   "id": "58da066eb9b43c80",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9229075908660889 0.1255202293395996\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "3.Numpy的ndarray的一维，二维的操作，常用属性，形状调整，与python list的互相转换",
   "id": "da97a094dbb33174"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:16:42.287008Z",
     "start_time": "2025-01-06T11:16:42.264034Z"
    }
   },
   "cell_type": "code",
   "source": [
    "t3 = np.arange(0, 10, 2)\n",
    "print(t3)\n",
    "print(\"-\" * 50)\n",
    "list2 = [[1, 2], [3, 4], [5, 6]]\n",
    "twoArray = np.array(list2)\n",
    "print(twoArray)"
   ],
   "id": "532adb171c81d032",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 4 6 8]\n",
      "--------------------------------------------------\n",
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:17:14.729448Z",
     "start_time": "2025-01-06T11:17:14.709423Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#常用属性\n",
    "list2 = [[1, 2], [3, 4], [5, 6]]\n",
    "twoArray = np.array(list2)\n",
    "# 获取数组的维度( 注意： 与函数的参数很像)\n",
    "print(twoArray.ndim)\n",
    "# 形状（行，列）\n",
    "print(twoArray.shape)\n",
    "# 有多少个元素\n",
    "print(twoArray.size)"
   ],
   "id": "1ab5cb2852084ea3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "(3, 2)\n",
      "6\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:17:48.609080Z",
     "start_time": "2025-01-06T11:17:48.589542Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#调整数组形状\n",
    "four = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "#修改的是原有的\n",
    "four.shape = (3, 2)\n",
    "print(four)\n",
    "#返回一个新的数组\n",
    "four = four.reshape(3, 2)\n",
    "print(four)\n",
    "#将多维变成一维数组\n",
    "five = four.reshape((6,), order='F')\n",
    "six = four.flatten(order='F')\n",
    "print(five)\n",
    "print(six)\n",
    "# 拓展：数组的形状\n",
    "t = np.arange(24)\n",
    "print(t)\n",
    "print(t.shape)\n",
    "# 转换成二维\n",
    "t1 = t.reshape((4, 6))\n",
    "print(t1)\n",
    "print(t1.shape)\n",
    "#转化成三维\n",
    "t2 = t.reshape((2, 3, 4))\n",
    "print(t2)\n",
    "print(t2.shape)"
   ],
   "id": "dab95eda15fb2e6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n",
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n",
      "[1 3 5 2 4 6]\n",
      "[1 3 5 2 4 6]\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
      "(24,)\n",
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]]\n",
      "(4, 6)\n",
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]\n",
      "\n",
      " [[12 13 14 15]\n",
      "  [16 17 18 19]\n",
      "  [20 21 22 23]]]\n",
      "(2, 3, 4)\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:18:18.921332Z",
     "start_time": "2025-01-06T11:18:18.902754Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#将数组转化成列表\n",
    "a = np.array([9, 12, 88, 14, 25])\n",
    "list_a = a.tolist()\n",
    "print(list_a)\n",
    "print(type(list_a))"
   ],
   "id": "1e51a1718ad6c3ea",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9, 12, 88, 14, 25]\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "4.掌握numpy的数据类型，练习数组和数的运算，数组和数组的运算，理解数组的轴",
   "id": "9435f80c62d6c11c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:19:14.330622Z",
     "start_time": "2025-01-06T11:19:14.316072Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#数组和数的计算\n",
    "t1 = np.arange(24).reshape((6, 4))\n",
    "print(t1)\n",
    "print(t1 + 2)\n",
    "print(\"-\" * 20)\n",
    "print(t1 * 2)\n",
    "print(\"-\" * 20)\n",
    "print(t1 / 2)"
   ],
   "id": "33cf8d4276502890",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n",
      "[[ 2  3  4  5]\n",
      " [ 6  7  8  9]\n",
      " [10 11 12 13]\n",
      " [14 15 16 17]\n",
      " [18 19 20 21]\n",
      " [22 23 24 25]]\n",
      "--------------------\n",
      "[[ 0  2  4  6]\n",
      " [ 8 10 12 14]\n",
      " [16 18 20 22]\n",
      " [24 26 28 30]\n",
      " [32 34 36 38]\n",
      " [40 42 44 46]]\n",
      "--------------------\n",
      "[[ 0.   0.5  1.   1.5]\n",
      " [ 2.   2.5  3.   3.5]\n",
      " [ 4.   4.5  5.   5.5]\n",
      " [ 6.   6.5  7.   7.5]\n",
      " [ 8.   8.5  9.   9.5]\n",
      " [10.  10.5 11.  11.5]]\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:19:45.124633Z",
     "start_time": "2025-01-06T11:19:45.115691Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#数组与数组之间的操作\n",
    "t1 = np.arange(24).reshape((6, 4))\n",
    "t2 = np.arange(100, 124).reshape((6, 4))\n",
    "print(t1 + t2)\n",
    "print(t1 * t2)"
   ],
   "id": "6c72b8e308d582bc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[100 102 104 106]\n",
      " [108 110 112 114]\n",
      " [116 118 120 122]\n",
      " [124 126 128 130]\n",
      " [132 134 136 138]\n",
      " [140 142 144 146]]\n",
      "[[   0  101  204  309]\n",
      " [ 416  525  636  749]\n",
      " [ 864  981 1100 1221]\n",
      " [1344 1469 1596 1725]\n",
      " [1856 1989 2124 2261]\n",
      " [2400 2541 2684 2829]]\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:20:07.787143Z",
     "start_time": "2025-01-06T11:20:07.778973Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#数组中的轴\n",
    "a = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(np.sum(a, axis=0))\n",
    "print(np.sum(a, axis=1))\n",
    "print(np.sum(a))"
   ],
   "id": "bec9209d4a2d1471",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 7 9]\n",
      "[ 6 15]\n",
      "21\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "5.使用数组的索引和切片，掌握数组数值修改",
   "id": "7564f2681e5a5717"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:20:56.562595Z",
     "start_time": "2025-01-06T11:20:56.542110Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#数组的索引和切片\n",
    "#一维数组\n",
    "a = np.arange(10)\n",
    "# 冒号分隔切片参数 start:stop:step 来进行切片操作 print(a[2:7:2])# 从索引 2 开始到索引 7 停止，间隔为 2\n",
    "# 如果只放置一个参数， 如 [2]， 将返回与该索引相对应的单个元素\n",
    "print(a[2], a)\n",
    "# 如果为 [2:]， 表示从该索引开始以后的所有项都将被提取\n",
    "print(a[2:])"
   ],
   "id": "58aec8d6b8a3837d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 [0 1 2 3 4 5 6 7 8 9]\n",
      "[2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:21:20.714227Z",
     "start_time": "2025-01-06T11:21:20.695802Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#多维数组\n",
    "t1 = np.arange(24).reshape(4, 6)\n",
    "print(t1)\n",
    "print('*' * 20)\n",
    "print(t1[1])  # 取一行(一行代表是一条数据， 索引也是从0开始的) print(t1[1,:]) # 取一行\n",
    "print('*' * 20)\n",
    "print(t1[1:])  # 取连续的多行\n",
    "print('*' * 20)\n",
    "print(t1[1:3, :])  # 取连续的多行\n",
    "print('*' * 20)\n",
    "print(t1[[0, 2, 3]])  # 取不连续的多行\n",
    "print('*' * 20)\n",
    "print(t1[[0, 2, 3], :])  # 取不连续的多行\n",
    "print('*' * 20)\n",
    "print(t1[:, 1])  # 取一列\n",
    "print('*' * 20)\n",
    "print(t1[:, 1:])  # 连续的多列\n",
    "print('*' * 20)\n",
    "print(t1[:, [0, 2, 3]])  # 取不连续的多列\n",
    "print('*' * 20)\n",
    "print(t1[2, 3])  # # 取某一个值,三行四列\n",
    "print('*' * 20)"
   ],
   "id": "e3af18c8cc352feb",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]]\n",
      "********************\n",
      "[ 6  7  8  9 10 11]\n",
      "********************\n",
      "[[ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]]\n",
      "********************\n",
      "[[ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]]\n",
      "********************\n",
      "[[ 0  1  2  3  4  5]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]]\n",
      "********************\n",
      "[[ 0  1  2  3  4  5]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]]\n",
      "********************\n",
      "[ 1  7 13 19]\n",
      "********************\n",
      "[[ 1  2  3  4  5]\n",
      " [ 7  8  9 10 11]\n",
      " [13 14 15 16 17]\n",
      " [19 20 21 22 23]]\n",
      "********************\n",
      "[[ 0  2  3]\n",
      " [ 6  8  9]\n",
      " [12 14 15]\n",
      " [18 20 21]]\n",
      "********************\n",
      "15\n",
      "********************\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T11:21:42.199285Z",
     "start_time": "2025-01-06T11:21:42.178922Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#数组中的数值修改\n",
    "t = np.arange(24).reshape(4, 6)\n",
    "# 修改某一行的值\n",
    "t[1, :] = 0\n",
    "# 修改某一列的值\n",
    "t[:, 1] = 0\n",
    "# 修改连续多行\n",
    "t[1:3, :] = 0\n",
    "# 修改连续多列\n",
    "t[:, 1:4] = 0\n",
    "# 修改多行多列， 取第二行到第四行， 第三列到第五列\n",
    "t[1:4, 2:5] = 0\n",
    "# 修改多个不相邻的点\n",
    "t[[0, 1], [0, 3]] = 0\n",
    "print(t)\n",
    "# 可以根据条件修改， 比如将小于 10 的值改掉\n",
    "t[t < 10] = 0\n",
    "print(t)\n",
    "# 使用逻辑判断\n",
    "# np.logical_and & # np.logical_or |\n",
    "# np.logical_not ~\n",
    "t[(t > 2) & (t < 6)] = 0  # 与\n",
    "t[(t < 2) | (t > 6)] = 0  # 或\n",
    "t[~(t > 6)] = 0  # 非\n",
    "print(t)\n",
    "t = t.clip(10, 18)\n",
    "print(t)"
   ],
   "id": "82ee4da8f77e0bc5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0  0  0  4  5]\n",
      " [ 0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0]\n",
      " [18  0  0  0  0 23]]\n",
      "[[ 0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0]\n",
      " [18  0  0  0  0 23]]\n",
      "[[0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0]]\n",
      "[[10 10 10 10 10 10]\n",
      " [10 10 10 10 10 10]\n",
      " [10 10 10 10 10 10]\n",
      " [10 10 10 10 10 10]]\n"
     ]
    }
   ],
   "execution_count": 22
  }
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